Recommendation device

ABSTRACT

A product selecting unit extracts a candidate for recommended product on the basis of browsing information in a browsing information storing unit and unpurchased product information in an unpurchased product information storing unit. Product specifications that are information specific to each product are stored in a product information storing unit. A specification correlation calculating unit extracts a product having a product specification correlated with a product specification of the candidate for recommended product on the basis of the product specifications, and extracts the product as a recommended product.

TECHNICAL FIELD

The present disclosure relates to a recommendation device that selectsadvertisements to be displayed in accordance with circumstances, inorder to display advertisements for either products that a user needstruly or products that are latently demanded as recommended products.

BACKGROUND ART

In order to display a product advertisement for a user of onlineshopping, a method of deriving a product advertisement to be displayedand its timing from the user's purchase information (including a historyor schedule) and the characteristics of products is typically used asdescribed in, for example, Patent Literature 1. In addition to thismethod, a method of determining a product advertisement to be displayedby referring to the purchase tendencies of other users having similarpurchase information is also known well.

CITATION LIST Patent Literature

-   Patent Literature 1: JP 2014-215772 A

SUMMARY OF INVENTION Technical Problem

However, in a conventional device, only similarity is acquired forproducts falling within a wide range corresponding to each category. Forexample, advertisements for smart phones currently being sold aredisplayed for a customer who purchased a smart phone three years ago.Further, in a conventional device, only similarity is acquired within anarrow range such as brand names and model numbers. For example, for acustomer who purchased a detergent one month ago, an advertisement forthe same detergent is displayed, and since a certain customer purchaseda product B together with a product A, an advertisement for B isdisplayed for another customer who purchased A.

Although these methods make it possible to select a very effectiveadvertisement under suitable circumstances, a problem with the methodsis that an unnecessary product advertisement is displayed with a highpossibility for a customer who is choosing furniture or a householdelectrical appliance, which is usually purchased at long time intervals,or a customer who is choosing a product with a specific intention ortaste. A problem is that a recommended product suitable for a targetcustomer is not necessarily provided, for example, an advertisement foran inexpensive watch is displayed for a customer who is looking for ahigh-class watch, or an advertisement for a PC that is not intended forgames is displayed for a customer who purchased a game-ready PC threeyears ago.

The present disclosure is made in order to solve the above-mentionedproblems, and it is therefore an object of the present disclosure toprovide a recommendation device that can improve the accuracy ofextraction of a recommended product.

Solution to Problem

A recommendation device according to the present disclosure includes: aproduct selecting unit for extracting a candidate for recommendedproduct that is to be presented to a target user on the basis ofbrowsing information indicating information about the user's browsing ofa product, and unpurchased product information indicating informationabout a product that the user has indicated an intention to purchase;and a specification correlation calculating unit for extracting aproduct having a product specification correlated with a productspecification of the candidate for recommended product, and extractingthe extracted product as a recommended product.

Advantageous Effects of Invention

The recommendation device according to the present disclosure extracts aproduct having a product specification correlated with a productspecification of a candidate for recommended product, and extracts thisproduct as a recommended product. As a result, the accuracy ofextraction of a recommended product can be improved.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a recommendation device of Embodiment1 of the present disclosure;

FIG. 2 is a hardware block diagram of the recommendation device ofEmbodiment 1 of the present disclosure;

FIG. 3 is a flowchart showing the operation of the recommendation deviceof Embodiment 1 of the present disclosure;

FIG. 4 is a flowchart showing extraction of recommended products whenreplacement is performed in a recommendation device of Embodiment 2 ofthe present disclosure; and

FIG. 5 is a flowchart showing extraction of recommended products whenlacking products are added in the recommendation device of Embodiment 2of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Hereafter, in order to explain the present disclosure in greater detail,embodiments of the present disclosure will be described with referenceto the accompanying drawings.

Embodiment 1

FIG. 1 is a block diagram of a recommendation delivery system includinga recommendation device according to this embodiment.

The illustrated recommendation delivery system includes a recommendationdelivery device 1, a network 2, and user terminals 3-1 to 3-n. Therecommendation delivery device 1 delivers advertisement informationabout products that users are demanding, to their respective userterminals 3-1 to 3-n via the network 2. In this configuration, one ormore users receive information about recommended products suited to eachof the users, such as product advertisements or news, via the network 2like the Internet, from the recommendation delivery device 1, by usingtheir respective user terminals 3-1 to 3-n each having an input andoutput function, each of the user terminals being a PC, a smart phone, aTV, or the like. At this time, information about recommended productsmay be displayed together on part of a screen of a browser, anapplication, or the like that is caused to display by a user, or may bedelivered in the form of an e-mail, a notification, or the like.Further, products mentioned here refer to what users can purchase anduse, such as electric appliances, clothing, food products, furniture,travel tours, or concert tickets.

The recommendation delivery device 1 includes a recommendationinformation delivery server 10, a recommendation device 11, a browsinginformation storing unit 12, an unpurchased product information storingunit 13, an owned product information storing unit 14, and a productinformation storing unit 15. The recommendation information deliveryserver 10 delivers recommendation information extracted by therecommendation device 11 to the user terminals 3-1 to 3-n. Therecommendation device 11 includes a product selecting unit 110 and aspecification correlation calculating unit 111, and extracts recommendedproducts. The product selecting unit 110 is a processing unit thatextracts a candidate for recommended product to a target user on thebasis of both browsing information in the browsing information storingunit 12 and unpurchased product information in the unpurchased productinformation storing unit 13. The specification correlation calculatingunit 111 is a processing unit that extracts a product having a productspecification correlated with a product specification of the candidatefor recommended product, the candidate being extracted by the productselecting unit 110, by referring to product specification information150 in the product information storing unit 15, and outputs the productas a recommended product.

The browsing information storing unit 12 is a storage unit that storesbrowsing information that is information about the type and contents ofa Web page, an application screen, or a document such as PDF that hasbeen caused to be displayed until now by a user. The unpurchased productinformation storing unit 13 is a storage unit that stores unpurchasedproduct information that is information about a product that a user hasindicated the user's intention to purchase, e.g., the user has paidattention to or has desired to purchase until now. In the unpurchasedproduct information, unlike the browsing information, the userexplicitly expresses information such as a fact that a product is notowned or an intention to purchase a product and the strength of theintention. As the simplest example of acquiring the unpurchased productinformation, there can be considered a method of allowing the user toselect a product that the user is interested in or a product that theuser desires to purchase from the product information hold by therecommendation delivery device 1. The owned product information storingunit 14 is a storage unit for storing owned product information that isinformation about a product that a user has purchased using onlineshopping or the like, or a product that the user owns. These browsinginformation, unpurchased product information, and owned productinformation may include information acquired from the outside of therecommendation delivery device 1, in addition information registered viathe recommendation delivery device 1. As examples of informationacquired from the outside, after a user's consent is acquired, thebrowsing information may be acquired from an external service such as asocial networking service (SNS), a browser, or a search engine, and theunpurchased product information may be linked to a questionnaire surveythat is performed by a third party, a wish list in a shopping site, orthe like. Further, after a user's consent is acquired, the owned productinformation may be inferred from credit card statements of the user,contents that the user has written on the Internet, etc. When acquiringthese pieces of information from the outside, the recommendationinformation delivery server 10 receives the pieces of information fromthe delivery sources of these pieces of information through theInternet. The product information storing unit 15 is a storage unit thatstores pieces of product information that are classified by theircategories, and has product specification information 150 as one type ofthe product information. The product specification information 150 maybe any type of information as long as the product specificationinformation shows information specific to a product, like the size, thespecifications, the color, the weight, or a function of the product.

Next, the hardware configuration of the recommendation device 11 will beexplained by referring to FIG. 2 .

The recommendation device 11 includes a processor 101, a memory 102, aninput and output interface 103, a storage 104, and a bus 105, as shownin FIG. 2 . The processor 101 implements the product selecting unit 110and the specification correlation calculating unit 111 by executingprograms corresponding to the functions of the product selecting unit110 and the specification correlation calculating unit 111. The memory102 is storage units, such as a ROM and a RAM, which are used as aprogram memory that stores various programs, a work memory that is usedwhen the processor 101 performs data processing, a memory in whichsignal data is developed, and so on. The input and output interface 103exchanges various types of signals with, for example, the browsinginformation storing unit 12, and the product information storing unit15, and the recommendation information delivery server 10. Further, thestorage 104 is a storage unit that stores a program corresponding toeach of the functions of the product selecting unit 110 and thespecification correlation calculating unit 111, and also stores varioustypes of data. The bus 105 is a communication path for mutuallyconnecting the processor 101, . . . , and the storage 104.

At least any one of the few product selecting unit 110 and thespecification correlation calculating units 111 may be constituted byhardware for exclusive use.

Next, the operation of the recommendation device of Embodiment 1 will beexplained.

First, the product selecting unit 110 extracts, as candidates forrecommended products, products that a target user is latently interestedin, by using the tendency of the user's previous actions that is basedon browsing information in the browsing information storing unit 12,information about products that the user has expressed interest in, theinformation being indicated by unpurchased product information in theunpurchased product information storing unit 13, and informationincluding purchase results or the likes based on owned productinformation in the owned product information storing unit 14. Further,as this extracting process, an analysis may be performed for acombination of the target user and another user. After extractingcandidates for recommended products, the product selecting unit 110outputs the candidates together with a message indicating that theseproducts have not been purchased to the specification correlationcalculating unit 111.

In the specification correlation calculating unit 111, recommendedproducts are determined using product specification information 150 inthe product information storing unit 15.

FIG. 3 is a flowchart showing the operation of the specificationcorrelation calculating unit 111. When receiving the candidates forrecommended products from the product selecting unit 110, thespecification correlation calculating unit 111 classifies the candidatesinto categories, by their product genres, in terms of price, size, andfunction (step ST101). At this time, the category classifying may beperformed by referring to product information in the product informationstoring unit 15. Next, the specification correlation calculating unit111 calculates, as to the candidate products that have been classifiedinto categories, the degree of correlation in product specificationsbetween candidate products (step ST103) for each of the categories (stepST102), and determines whether the correlation degree exceeds a setthreshold (step ST104). This is a process of calculating what degree ofcorrelation two or more candidate products classified into the samecategory have with respect to their specifications, and finding out acategory having a stronger correlation. Because it is expected that thecategory that has been determined to have a strong correlation (YES instep ST104) exhibits the user's taste, the category (condition) is addedto conditions for candidate selection (step ST105). The specificationcorrelation calculating unit 111 then determines whether the calculationhas been performed on all the categories (step ST106), and, whendetermines that the calculation has not been performed on all thecategories (NO in step ST106), the processing returns to step ST102 andthe processes of steps ST102 to ST105 are repeated. In contrast, when,in step ST104, the correlation degree is equal to or less than thethreshold (NO in step ST104), the processing just shifts to step ST106.

In a stage in which all conditions for candidate selection arecompletely provided (YES in step ST106), the specification correlationcalculating unit 111, for example, narrows down the candidate productson the basis of the conditions for candidate selection in such a waythat the conditions are satisfied, or adds, as a candidate product,another product satisfying the conditions on the basis of the productinformation in the product information storing unit 15, therebyextracting recommended products (step ST107).

Although the specification correlation calculating unit 111 extractsrecommended products on the basis of the conditions for candidateselection as mentioned above, the conditions for candidate selection maybe outputted to the product selecting unit 110, and the productselecting unit 110 may select recommended products each satisfying theconditions. Further, as a condition applied to the recommended products,hot-selling products, other users' evaluations, etc. may be referred to.

As mentioned above, in Embodiment 1, as the processing of the productselecting unit 110, products that a user needs explicitly or latentlyare selected on the basis of the user's actions, indication of theuser's intention, etc., just as in conventional cases. Then, because onthe basis of the specifications of the products selected by the productselecting unit 110, the specification correlation calculating unit 111can perform the processing independently of apart in which the narrowingdown or addition of a high-relevant product is performed, broadapplication can be expected as an extension of the existingrecommendation methods.

Further, as an advantage of the extraction of recommended products,although information such as the product specification information 150is additionally needed, unlike in the case of previous recommendationmethods, the addition of the information makes it possible to catch auser's taste more finely than in the previous recommendation methods.For example, when a user thinks that a function is unnecessary, and islooking for products to which the function is not added, there's noother choice but to check the specifications of each product until nowbecause it is difficult to make a search for products by using a keyword “without oo function”. The use of this recommendation device makesit possible to infer that a user has intensively checked productswithout oo function from the user's actions or indication of the user'sintention, and therefore the user is enabled to efficiently find outproducts that the user is demanding, in response to the recommendationof products not equipped with oo function.

As previously explained, because the recommendation device of Embodiment1 includes the product selecting unit for extracting a candidate forrecommended product that is to be presented to a target user on thebasis of browsing information indicating information about the user'sbrowsing of a product, and unpurchased product information indicatinginformation about a product that the user has indicated an intention topurchase; and the specification correlation calculating unit forextracting a product having a product specification correlated with aproduct specification of the candidate for recommended product, andextracting the extracted product as a recommended product, the accuracyof extraction of a recommended product can be improved.

Embodiment 2

Embodiment 2 relates to a configuration in which a user's next purchasetendency is predicted from owned product information that is historyinformation about products owned by the user. Because the configurationin terms of drawings is the same as that of Embodiment 1 shown in FIG. 1, the configuration will be explained using FIG. 1 .

A product selecting unit 110 in Embodiment 2 has a function of acquiringinformation about owned products of a target user on the basis of ownedproduct information in an owned product information storing unit 14, inaddition to the function of Embodiment 1. Further, a specificationcorrelation calculating unit 111 is configured so as to, in addition tohaving the function of Embodiment 1, when extracting a recommendedproduct that is a target for replacement of an owned product about whichinformation is acquired by the product selecting unit 110, extract aproduct having a product specification correlated with a productspecification to be maintained or improved, out of the productspecifications of the owned product, and, when extracting a recommendedproduct for compensating for lack of owned products, compare informationabout a product group which is stored in a product information storingunit 15 and the information about the owned products, and extract, as alacking product, a product that is not included in the product group,and also determine a product specification of this lacking product fromthe product specifications of the owned products and extract a productmatching the determined product specification. Further, in the productinformation storing unit 15, information about a product group, theinformation indicating a combination of products that is determined onthe basis of a predetermined condition, is stored. Because aconfiguration other than this configuration is the same as that ofEmbodiment 1, an explanation will be omitted hereafter.

Next, the operation of a recommendation device of Embodiment 2 will beexplained.

First, the product selecting unit 110, just as in conventional cases,notifies the specification correlation calculating unit 111 ofinformation about products currently owned by a user, together with amessage indicating that the products are owned, on the basis of ownedproduct information in the owned product information storing unit 14,the owned product information indicating the user's history of previouspurchases and the user's own products. When receiving the informationabout the owned products, the specification correlation calculating unit111 performs processing for two patterns including replacement purchaseand additional purchase which are shown below.

When Replacement of an Owned Product is Considered

A flowchart of the operation of the specification correlationcalculating unit 111 when replacement of an owned product is consideredis shown in FIG. 4 .

When a user who considers purchasing a new product for the reason thatthe user's own product has become old or has been broken down isassumed, the specification correlation calculating unit 111 selectsproducts that are candidates for replacement by performing the followingprocessing. First, as to all the user's own products, it is determinedwhether or not the replacement of each of the products is desirable,from a purchase time and a time interval for replacement purchase of thecorresponding product when the purchase time is known, or from a salesperiod and a time interval for replacement purchase of the correspondingproduct when the purchase time is unknown (step ST201). Thisdetermination is performed for each of the user's own products (stepST202). When the replacement is not desirable for an owned product thatis a determination object (when NO in step ST202), the processingreturns to step ST201 and the replacement determination is performed onthe next owned product.

As to a product that has been determined, in step ST202, to be desirableto be replaced (YES in step ST202), it is then retrieved whether aspecification of the product should be maintained or improved when theproduct is replaced, from the type of the product, together with thespecification of the product, from the product information in theproduct information storing unit 15 (steps ST203 and ST204). Forexample, in general, the “size” in the specifications of furniture or ahousehold electrical appliance should be maintained, and an improvementin another specification such as performance or capability is demandedwhen the product is replaced. When a specification should be maintainedor improved (YES in step ST204), the specification is added as amaintenance or improvement condition (step ST205). In contrast, when aspecification should not be maintained or improved (NO in step ST204),the specification, as a specification condition, is not added toconditions for candidate selection. The addition of a specification thatshould be maintained may be indispensable, and the addition of aspecification that should be improved may be free. Next, thespecification correlation calculating unit 111 whether or not all thespecifications have been processed (step ST206), and, when aspecification on which the determination should be performed stillremains (NO in step ST206), the processing returns to step ST203 and theprocesses of steps ST203 to ST205 are repeated.

When the determination has been performed on all the specifications instep ST206 (YES in step ST206), the specification correlationcalculating unit 111 extracts products under the conditions forcandidate selection (step ST207). More specifically, products that arerecommended to be used for replacement and each of which satisfieseither a condition to be maintained or a condition to be improved areselected from the product information. After that, whether or not allthe products have been processed is determined (step ST208), and, when ayet-to-be-processed product remains (NO in step ST208), the processingreturns to the step ST201 and the above-mentioned processes arerepeated, whereas when it is determined that all the products have beenprocessed (YES in step ST208), the product replacement determinationprocessing is ended.

In the determination of whether or not the replacement is desirable, outof the above-mentioned processes, an inference that a malfunction mayhave occurred in a product may be used, on the basis of information asthe user's browsing information in a browsing information storing unit12, the information indicating that the user has referred to informationabout troubleshooting for the product, or the like. Further, informationindicating that another user has replaced the same product by a newproduct may be used. In addition, the selection of products eachsatisfying a condition may be performed by the product selecting unit110 after the conditions for candidate selection are sent to the productselecting unit 110.

When Compensation for Lack of the Owned Products is Considered

A flowchart of the operation of the specification correlationcalculating unit 111 when compensation for lack of the owned products isconsidered is shown in FIG. 5 .

Irrespective of whether or not a user satisfies, the specificationcorrelation calculating unit 111 selects, as a product that is acandidate for additional purchase, a product lacking in the ownedproducts of the user. In this case, it is assumed that information abouta group of combined products is registered in advance as productinformation in the product information storing unit 15. For example, inthe case of household electrical appliances, it is assumed that arefrigerator, a washing machine, a cleaner, a television, and so on areregistered as a group. By using this product information, thespecification correlation calculating unit 111 first compares the user'sown products and the product group (step ST301). When it is determined,as a result of the comparison, that there are lacking products, i.e.,products that the user has not owned or purchased (YES in step ST302),out of the lacking products, as to a product for which the user has notindicated unnecessity, a condition for a product that is inferred to beappropriate to be additionally purchased from the specifications of theuser's own products included in the group is determined (step ST303).For example, when the user has purchased a refrigerator, a washingmachine, and a cleaner made by AA Electric Corporation, a televisionmade by AA Electric Corporation are determined as a condition. Next, onthe basis of the derived conditions, the specification correlationcalculating unit 111 extracts products satisfying the conditions, fromthe product information stored in the product information storing unit15, just as in the case of replacement (step ST304). Then, whether ornot the comparison has been performed on all the owned products isdetermined (step ST305), and the processing is ended when thedetermination has been performed on all the owned products (YES in stepST305). In contrast, when an owned product as an object for comparisonremains in step ST305 (NO in step ST305), the processing returns to stepST301 and the above-mentioned processes are repeated. Further, whenthere is no lacking product in step ST302, the processing is just ended.

In this way, in this embodiment, by adding the process using thecorrelation degrees in product specifications, to the conventionalprocess of extracting recommended products while setting them as targetsfor replacement purchase or additional purchase, it is possible to morecorrectly select products that the user is demanding, as an extension ofthe existing systems. Concretely, recommendations to the user'spurchases over a long period of time, for example, are provided inconsideration of the user's environment (residential space, familystructure, economic conditions, etc.), a conventional recommendationmethod being weak in providing such recommendations, so that a productthat is purchased as a replacement or additionally purchased hasspecifications (size, specifications, price, etc.) matching theenvironment.

As previously explained, in the recommendation device of Embodiment 2,because the product selecting unit acquires information about one ormore owned products of a target user on the basis of owned productinformation indicating information about the products owned by the user,and when extracting a recommended product that is a target forreplacement of an owned product of the target user, the specificationcorrelation calculating unit extracts a product having a productspecification correlated with a product specification to be maintained,out of product specifications of the owned product, the accuracy ofextraction of a recommended product at a time of replacement purchasecan be improved.

Further, in the recommendation device of Embodiment 2, because thespecification correlation calculating unit extracts a product having aproduct specification correlated with a product specification to beimproved, out of the product specifications of the owned product, arecommended product corresponding to a product that a user is demandingcan be extracted with a higher degree of accuracy.

Further, in the recommendation device of Embodiment 2, because theproduct selecting unit acquires information about one or more ownedproducts of the target user on the basis of owned product informationindicating information about the products owned by the user, and whenextracting a recommended product for compensating for lack of the ownedproducts of the target user, the specification correlation calculatingunit compares a product group indicating a combination of products andthe owned products and extracts, as a lacking product, a product that isnot included in the product group, and also determines a productspecification of the lacking product from product specifications of theowned products and extracts a product matching the productspecification, the accuracy of extraction of a recommended product at atime of additional purchase can be improved.

Further, in the recommendation device of Embodiment 2, because theproduct selecting unit acquires information about one or more ownedproducts that the target user has purchased on the basis of ownedproduct information indicating information about the products owned bythe user, and when extracting a recommended product that is a target forreplacement of an owned product of the target user, the specificationcorrelation calculating unit extracts a product having a productspecification correlated with a product specification to be maintained,out of product specifications of the owned product, and when extractinga recommended product for compensating for lack of the owned products ofthe target user, the specification correlation calculating unit comparesa product group indicating a combination of products and the ownedproducts and extracts a lacking product, and also determines a productspecification of the lacking product from product specifications of theowned products and extracts a product matching the productspecification, the accuracy of extraction of a recommended product at atime of replacement purchase and at a time of additional purchase can beimproved.

It is to be understood that any combination of the above-mentionedembodiments can be made, various changes can be made in any componentaccording to any one of the above-mentioned embodiments, and anycomponent according to any one of the above-mentioned embodiments can beomitted within the scope of the present disclosure.

INDUSTRIAL APPLICABILITY

As mentioned above, the recommendation device according to the presentdisclosure relates to a configuration in which a recommendation deviceis extracted on the basis of browsing information and unpurchasedproduct information about users, and product specifications, and issuitable for providing product advertisements for users in onlineshopping.

REFERENCE SIGNS LIST

1 recommendation delivery device, 2 network, 3-1 to 3-n user terminal,10 recommendation information delivery server, 11 recommendation device,12 browsing information storing unit, 13 unpurchased product informationstoring unit, 14 owned product information storing unit, 15 productinformation storing unit, 110 product selecting unit, 111 specificationcorrelation calculating unit, and 150 product specification information.

1.-5. (canceled)
 6. An apparatus comprising at least one processor andat least one memory including computer program code, the at least onememory and the computer program code configured to, with the at leastone processor, cause the apparatus to: receive browsing informationassociated with a first user, wherein the browsing information comprisesinformation indicating that the first user has referred to informationassociated with troubleshooting a malfunction associated with a firstproduct; determine that the first product is affected by the malfunctionbased at least on the browsing information; determine a plurality ofcandidates for a first recommended product for the first user based atleast on the determination that the first product is affected by themalfunction; determining a first recommended product from the pluralityof candidates; and cause the first recommended product to be presentedto the first user.
 7. The apparatus of claim 6, wherein the firstrecommended product is a replacement for the first product affected bythe malfunction.
 8. The apparatus of claim 6, wherein the determinationof the first recommended product for the first user is further based atleast on information indicating that a second user has replaced a secondproduct with a second recommended product.
 9. The apparatus of claim 8,wherein the second product is identical to the first product and thesecond recommended product is identical to the first recommendedproduct.
 10. The apparatus of claim 6, wherein the at least one memoryand the computer program code are further configured to, with the atleast one processor, cause the apparatus to capture the browsinginformation via a user interface of a user device associated with thefirst user.
 11. The apparatus of claim 6, wherein the at least onememory and the computer program code are further configured to, with theat least one processor, cause the apparatus to determine a product grouptype associated with the first recommended product; determine a thirdrecommended product for the first user based at least on the productgroup type associated with the first recommended product; and cause thethird recommended product to be presented to the first user.
 12. Theapparatus of claim 6, wherein the determination of the plurality ofcandidates is based at least on historical browsing informationassociated with the first user.
 13. The apparatus of claim 12, whereinthe determination of the plurality of candidates is further based atleast on historical browsing information associated with at least oneother user.
 14. The apparatus of claim 6, wherein the at least onememory and the computer program code are further configured to, with theat least one processor, cause the apparatus to determine a plurality ofcategories associated with each candidate of the plurality ofcandidates; and select at least one category to define a condition forcandidate selection, wherein determination of the first recommendedproduct is further based at least on the at least one category.
 15. Amethod comprising: receiving browsing information associated with afirst user, wherein the browsing information comprises informationindicating that the first user has referred to information associatedwith troubleshooting a malfunction associated with a first product;determining that the first product is affected by the malfunction basedat least on the browsing information; determining a plurality ofcandidates for a first recommended product for the first user based atleast on the determination that the first product is affected by themalfunction; determining a first recommended product from the pluralityof candidates; and causing the first recommended product to be presentedto the first user.
 16. The method of claim 15, wherein the firstrecommended product is a replacement for the first product affected bythe malfunction.
 17. The method of claim 15, wherein the determinationof the first recommended product for the first user is further based atleast on information indicating that a second user has replaced a secondproduct with a second recommended product.
 18. The method of claim 17,wherein the second product is identical to the first product and thesecond recommended product is identical to the first recommendedproduct.
 19. The method of claim 17, wherein the second product isaffected by the malfunction.
 20. The method of claim 15, furthercomprising: capturing the browsing information via a user interface of auser device associated with the first user.
 21. The method of claim 15,further comprising: determining a product group type associated with thefirst recommended product; determining a third recommended product forthe first user based at least on the product group type associated withthe first recommended product; and causing the third recommended productto be presented to the first user.
 22. The method of claim 15, whereinthe determination of the plurality of candidates is based at least onhistorical browsing information associated with the first user.
 23. Themethod of claim 22, wherein the determination of the plurality ofcandidates is further based at least on historical browsing informationassociated with at least one other user.
 24. The method of claim 15,further comprising: determining a plurality of categories associatedwith each candidate of the plurality of candidates; and selecting atleast one category to define a condition for candidate selection,wherein determination of the first recommended product is further basedat least on the at least one category.
 25. At least one non-transitorycomputer-readable storage medium having computer-executable program codeportions stored therein, the computer-executable program code portionscomprising program code instructions configured to: receive browsinginformation associated with a first user, wherein the browsinginformation comprises information indicating that the first user hasreferred to information associated with troubleshooting a malfunctionassociated with a first product; determine that the first product isaffected by the malfunction based at least on the browsing information;determine a plurality of candidates for a first recommended product forthe first user based at least on the determination that the firstproduct is affected by the malfunction; determining a first recommendedproduct from the plurality of candidates; and cause the firstrecommended product to be presented to the first user.